GPR Data Reconstruction Using Residual Feature Distillation Block U-Net
发布时间：2025-05-28
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所属单位：中南大学
发表刊物：IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
关键字：Deep learning, ground penetrating radar (GPR), missing traces, reconstruction, residual feature distillation block U-Net (RFDB-U-net).
摘要：Due to the unevenness of ground surface, mismatch between trig interval and sampling speed,or other electromagnetic interferences, traces missing is a quite typical occurrence during the on-ground ground penetrating radar (GPR) testing. Effective reconstruction of GPR missing traces has been regarded a crucial link to improve both the signal-to-noise ratio of raw data and the resolution of GPR imaging. In this article, we propose a novel deep-learning framework based on the residual feature distillation block U-Net (RFDB-U-Net) to mitigate the transmission loss problem of the conventional U-Net. To be specific, by employing the information distillation network based on the multiple feature extraction connections, RFDB is capable of utilizing the adequate residual information of each layer for feature learning. Moreover,a skip connection is additional patched on the residual units to properly compensate the missing features in the convolution process. In particular, the merging of lightweight U-Net ensures the lightnessof RFDB. The outperformance of the proposed framework is verified in detail through the reconstruction accuracy and evaluation metrics in the test of synthetic data, laboratorial data, and in-site field data.
合写作者：Yue He, Jianwei Lei, Xiangyu Wang
第一作者：Qianwei Dai
论文类型：期刊论文
通讯作者：Yi Lei, Bin Zhang
是否译文：否
收录刊物：SCI